mirror of https://github.com/open-mmlab/mmyolo.git
200 lines
7.8 KiB
Python
200 lines
7.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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from collections import OrderedDict
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import torch
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convert_dict = {
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# stem
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'model.0': 'backbone.stem.0',
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'model.1': 'backbone.stem.1',
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'model.2': 'backbone.stem.2',
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# stage1
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# ConvModule
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'model.3': 'backbone.stage1.0',
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# ELANBlock expand_channel_2x
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'model.4': 'backbone.stage1.1.short_conv',
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'model.5': 'backbone.stage1.1.main_conv',
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'model.6': 'backbone.stage1.1.blocks.0.0',
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'model.7': 'backbone.stage1.1.blocks.0.1',
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'model.8': 'backbone.stage1.1.blocks.1.0',
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'model.9': 'backbone.stage1.1.blocks.1.1',
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'model.11': 'backbone.stage1.1.final_conv',
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# stage2
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# MaxPoolBlock reduce_channel_2x
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'model.13': 'backbone.stage2.0.maxpool_branches.1',
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'model.14': 'backbone.stage2.0.stride_conv_branches.0',
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'model.15': 'backbone.stage2.0.stride_conv_branches.1',
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# ELANBlock expand_channel_2x
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'model.17': 'backbone.stage2.1.short_conv',
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'model.18': 'backbone.stage2.1.main_conv',
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'model.19': 'backbone.stage2.1.blocks.0.0',
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'model.20': 'backbone.stage2.1.blocks.0.1',
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'model.21': 'backbone.stage2.1.blocks.1.0',
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'model.22': 'backbone.stage2.1.blocks.1.1',
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'model.24': 'backbone.stage2.1.final_conv',
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# stage3
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# MaxPoolBlock reduce_channel_2x
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'model.26': 'backbone.stage3.0.maxpool_branches.1',
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'model.27': 'backbone.stage3.0.stride_conv_branches.0',
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'model.28': 'backbone.stage3.0.stride_conv_branches.1',
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# ELANBlock expand_channel_2x
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'model.30': 'backbone.stage3.1.short_conv',
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'model.31': 'backbone.stage3.1.main_conv',
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'model.32': 'backbone.stage3.1.blocks.0.0',
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'model.33': 'backbone.stage3.1.blocks.0.1',
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'model.34': 'backbone.stage3.1.blocks.1.0',
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'model.35': 'backbone.stage3.1.blocks.1.1',
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'model.37': 'backbone.stage3.1.final_conv',
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# stage4
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# MaxPoolBlock reduce_channel_2x
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'model.39': 'backbone.stage4.0.maxpool_branches.1',
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'model.40': 'backbone.stage4.0.stride_conv_branches.0',
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'model.41': 'backbone.stage4.0.stride_conv_branches.1',
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# ELANBlock no_change_channel
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'model.43': 'backbone.stage4.1.short_conv',
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'model.44': 'backbone.stage4.1.main_conv',
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'model.45': 'backbone.stage4.1.blocks.0.0',
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'model.46': 'backbone.stage4.1.blocks.0.1',
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'model.47': 'backbone.stage4.1.blocks.1.0',
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'model.48': 'backbone.stage4.1.blocks.1.1',
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'model.50': 'backbone.stage4.1.final_conv',
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# neck SPPCSPBlock
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'model.51.cv1': 'neck.reduce_layers.2.main_layers.0',
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'model.51.cv3': 'neck.reduce_layers.2.main_layers.1',
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'model.51.cv4': 'neck.reduce_layers.2.main_layers.2',
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'model.51.cv5': 'neck.reduce_layers.2.fuse_layers.0',
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'model.51.cv6': 'neck.reduce_layers.2.fuse_layers.1',
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'model.51.cv2': 'neck.reduce_layers.2.short_layers',
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'model.51.cv7': 'neck.reduce_layers.2.final_conv',
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# neck
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'model.52': 'neck.upsample_layers.0.0',
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'model.54': 'neck.reduce_layers.1',
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# neck ELANBlock reduce_channel_2x
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'model.56': 'neck.top_down_layers.0.short_conv',
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'model.57': 'neck.top_down_layers.0.main_conv',
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'model.58': 'neck.top_down_layers.0.blocks.0',
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'model.59': 'neck.top_down_layers.0.blocks.1',
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'model.60': 'neck.top_down_layers.0.blocks.2',
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'model.61': 'neck.top_down_layers.0.blocks.3',
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'model.63': 'neck.top_down_layers.0.final_conv',
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'model.64': 'neck.upsample_layers.1.0',
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'model.66': 'neck.reduce_layers.0',
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# neck ELANBlock reduce_channel_2x
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'model.68': 'neck.top_down_layers.1.short_conv',
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'model.69': 'neck.top_down_layers.1.main_conv',
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'model.70': 'neck.top_down_layers.1.blocks.0',
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'model.71': 'neck.top_down_layers.1.blocks.1',
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'model.72': 'neck.top_down_layers.1.blocks.2',
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'model.73': 'neck.top_down_layers.1.blocks.3',
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'model.75': 'neck.top_down_layers.1.final_conv',
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# neck MaxPoolBlock no_change_channel
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'model.77': 'neck.downsample_layers.0.maxpool_branches.1',
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'model.78': 'neck.downsample_layers.0.stride_conv_branches.0',
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'model.79': 'neck.downsample_layers.0.stride_conv_branches.1',
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# neck ELANBlock reduce_channel_2x
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'model.81': 'neck.bottom_up_layers.0.short_conv',
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'model.82': 'neck.bottom_up_layers.0.main_conv',
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'model.83': 'neck.bottom_up_layers.0.blocks.0',
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'model.84': 'neck.bottom_up_layers.0.blocks.1',
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'model.85': 'neck.bottom_up_layers.0.blocks.2',
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'model.86': 'neck.bottom_up_layers.0.blocks.3',
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'model.88': 'neck.bottom_up_layers.0.final_conv',
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# neck MaxPoolBlock no_change_channel
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'model.90': 'neck.downsample_layers.1.maxpool_branches.1',
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'model.91': 'neck.downsample_layers.1.stride_conv_branches.0',
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'model.92': 'neck.downsample_layers.1.stride_conv_branches.1',
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# neck ELANBlock reduce_channel_2x
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'model.94': 'neck.bottom_up_layers.1.short_conv',
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'model.95': 'neck.bottom_up_layers.1.main_conv',
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'model.96': 'neck.bottom_up_layers.1.blocks.0',
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'model.97': 'neck.bottom_up_layers.1.blocks.1',
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'model.98': 'neck.bottom_up_layers.1.blocks.2',
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'model.99': 'neck.bottom_up_layers.1.blocks.3',
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'model.101': 'neck.bottom_up_layers.1.final_conv',
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# RepVGGBlock
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'model.102.rbr_dense.0': 'neck.out_layers.0.rbr_dense.conv',
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'model.102.rbr_dense.1': 'neck.out_layers.0.rbr_dense.bn',
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'model.102.rbr_1x1.0': 'neck.out_layers.0.rbr_1x1.conv',
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'model.102.rbr_1x1.1': 'neck.out_layers.0.rbr_1x1.bn',
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'model.103.rbr_dense.0': 'neck.out_layers.1.rbr_dense.conv',
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'model.103.rbr_dense.1': 'neck.out_layers.1.rbr_dense.bn',
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'model.103.rbr_1x1.0': 'neck.out_layers.1.rbr_1x1.conv',
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'model.103.rbr_1x1.1': 'neck.out_layers.1.rbr_1x1.bn',
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'model.104.rbr_dense.0': 'neck.out_layers.2.rbr_dense.conv',
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'model.104.rbr_dense.1': 'neck.out_layers.2.rbr_dense.bn',
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'model.104.rbr_1x1.0': 'neck.out_layers.2.rbr_1x1.conv',
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'model.104.rbr_1x1.1': 'neck.out_layers.2.rbr_1x1.bn',
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# head
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'model.105.m': 'bbox_head.head_module.convs_pred'
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}
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def convert(src, dst):
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"""Convert keys in detectron pretrained YOLOv7 models to mmyolo style."""
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try:
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yolov7_model = torch.load(src)['model'].float()
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blobs = yolov7_model.state_dict()
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except ModuleNotFoundError:
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raise RuntimeError(
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'This script must be placed under the WongKinYiu/yolov7 repo,'
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' because loading the official pretrained model need'
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' `model.py` to build model.')
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state_dict = OrderedDict()
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for key, weight in blobs.items():
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if key.find('anchors') >= 0 or key.find('anchor_grid') >= 0:
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continue
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num, module = key.split('.')[1:3]
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if int(num) < 102 and int(num) != 51:
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prefix = f'model.{num}'
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new_key = key.replace(prefix, convert_dict[prefix])
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state_dict[new_key] = weight
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print(f'Convert {key} to {new_key}')
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elif int(num) < 105 and int(num) != 51:
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strs_key = key.split('.')[:4]
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new_key = key.replace('.'.join(strs_key),
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convert_dict['.'.join(strs_key)])
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state_dict[new_key] = weight
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print(f'Convert {key} to {new_key}')
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else:
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strs_key = key.split('.')[:3]
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new_key = key.replace('.'.join(strs_key),
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convert_dict['.'.join(strs_key)])
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state_dict[new_key] = weight
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print(f'Convert {key} to {new_key}')
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# save checkpoint
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checkpoint = dict()
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checkpoint['state_dict'] = state_dict
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torch.save(checkpoint, dst)
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# Note: This script must be placed under the yolov7 repo to run.
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def main():
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parser = argparse.ArgumentParser(description='Convert model keys')
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parser.add_argument(
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'--src', default='yolov7.pt', help='src yolov7 model path')
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parser.add_argument('--dst', default='mm_yolov7l.pt', help='save path')
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args = parser.parse_args()
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convert(args.src, args.dst)
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if __name__ == '__main__':
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main()
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